Client Report - The War with Star Wars

Table of contents

• Elevator pitch
• QUESTION|TASK 1
• QUESTION|TASK 2
  • Step 2.1 — Filter to respondents who have seen at least one film
  • Step 2.2 — Convert age ranges to numeric values
  • Step 2.3 — Convert education levels to numeric values
  • Step 2.4 — Convert income ranges to numeric values
  • Step 2.5 — Create the target column for prediction
  • Step 2.6 — One-hot encode remaining categorical variables
• QUESTION|TASK 3
  • Visual #1 — Average ranking of Star Wars movies
  • Visual #2 — “Who shot first?” responses
• QUESTION|TASK 4

import pandas as pd 
import numpy as np
import sklearn
from lets_plot import *
LetsPlot.setup_html(isolated_frame=True)

print("Pandas:", pd.__version__)
print("NumPy:", np.__version__)
print("sklearn:", sklearn.__version__)

url = "https://raw.githubusercontent.com/fivethirtyeight/data/master/star-wars-survey/StarWars.csv"
df = pd.read_csv(url, encoding="ISO-8859-1")

df.head()  

Pandas: 2.3.2
NumPy: 2.3.3
sklearn: 1.7.2

	RespondentID	Have you seen any of the 6 films in the Star Wars franchise?	Do you consider yourself to be a fan of the Star Wars film franchise?	Which of the following Star Wars films have you seen? Please select all that apply.	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.	...	Unnamed: 28	Which character shot first?	Are you familiar with the Expanded Universe?	Do you consider yourself to be a fan of the Expanded Universe?ξ	Do you consider yourself to be a fan of the Star Trek franchise?	Gender	Age	Household Income	Education	Location (Census Region)
0	NaN	Response	Response	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	Star Wars: Episode IV A New Hope	Star Wars: Episode V The Empire Strikes Back	Star Wars: Episode VI Return of the Jedi	Star Wars: Episode I The Phantom Menace	...	Yoda	Response	Response	Response	Response	Response	Response	Response	Response	Response
1	3.292880e+09	Yes	Yes	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	Star Wars: Episode IV A New Hope	Star Wars: Episode V The Empire Strikes Back	Star Wars: Episode VI Return of the Jedi	3	...	Very favorably	I don't understand this question	Yes	No	No	Male	18-29	NaN	High school degree	South Atlantic
2	3.292880e+09	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	Yes	Male	18-29	$0 - $24,999	Bachelor degree	West South Central
3	3.292765e+09	Yes	No	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	NaN	NaN	NaN	1	...	Unfamiliar (N/A)	I don't understand this question	No	NaN	No	Male	18-29	$0 - $24,999	High school degree	West North Central
4	3.292763e+09	Yes	Yes	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	Star Wars: Episode IV A New Hope	Star Wars: Episode V The Empire Strikes Back	Star Wars: Episode VI Return of the Jedi	5	...	Very favorably	I don't understand this question	No	NaN	Yes	Male	18-29	$100,000 - $149,999	Some college or Associate degree	West North Central

5 rows × 38 columns

# Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here

# import your data here using pandas and the URL

Elevator pitch

A SHORT (2-3 SENTENCES) PARAGRAPH THAT DESCRIBES KEY INSIGHTS TAKEN FROM METRICS IN THE PROJECT RESULTS THINK TOP OR MOST IMPORTANT RESULTS. (Note: this is not a summary of the project, but a summary of the results.)

A Client has requested this analysis and this is your one shot of what you would say to your boss in a 2 min elevator ride before he takes your report and hands it to the client.

QUESTION|TASK 1

Shorten the column names and clean them up for easier use with pandas. Provide a table or list that exemplifies how you fixed the names.

The original dataset contained long survey question text and many “Unnamed” auto-generated column labels. I cleaned the column names using a mapping dictionary to shorten the labels and make them easier to use for modeling. Below is a sample of the renaming applied:

Original Column Name	Clean Name
Have you seen any of the 6 films…	seen_any
Do you consider yourself a fan of Star Wars	fan_starwars
Which films have you seen…	seen_films
Unnamed: 4 → Unnamed: 8	seen_ep1 → seen_ep5
Ranking question	rank_ep1 → rank_ep6
Character favorability	char_luke → char_mace
Gender	gender
Age	age_range
Household Income	income_range
Education	education
Location (Census Region)	location

rename_map = {
    "RespondentID": "respondent_id",
    "Have you seen any of the 6 films in the Star Wars franchise?": "seen_any",
    "Do you consider yourself to be a fan of the Star Wars film franchise?": "fan_starwars",
    "Which of the following Star Wars films have you seen? Please select all that apply.": "seen_films",
    "Unnamed: 4": "seen_ep1",
    "Unnamed: 5": "seen_ep2",
    "Unnamed: 6": "seen_ep3",
    "Unnamed: 7": "seen_ep4",
    "Unnamed: 8": "seen_ep5",
    "Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.": "rank_ep1",
    "Unnamed: 10": "rank_ep2",
    "Unnamed: 11": "rank_ep3",
    "Unnamed: 12": "rank_ep4",
    "Unnamed: 13": "rank_ep5",
    "Unnamed: 14": "rank_ep6",
    "Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.": "char_luke",
    "Unnamed: 16": "char_han",
    "Unnamed: 17": "char_leia",
    "Unnamed: 18": "char_anakin",
    "Unnamed: 19": "char_obiwan",
    "Unnamed: 20": "char_emperor",
    "Unnamed: 21": "char_darthmaul",
    "Unnamed: 22": "char_yoda",
    "Unnamed: 23": "char_boba",
    "Unnamed: 24": "char_jabba",
    "Unnamed: 25": "char_padme",
    "Unnamed: 26": "char_jarjar",
    "Unnamed: 27": "char_palpatine",
    "Unnamed: 28": "char_mace",
    "Which character shot first?": "shot_first",
    "Are you familiar with the Expanded Universe?": "know_eu",
    "Do you consider yourself to be a fan of the Expanded Universe?": "fan_eu",
    "Do you consider yourself to be a fan of the Star Trek franchise?": "fan_startrek",
    "Gender": "gender",
    "Age": "age_range",
    "Household Income": "income_range",
    "Education": "education",
    "Location (Census Region)": "location"
}

df = df.rename(columns=rename_map)

df.head()

	respondent_id	seen_any	fan_starwars	seen_films	seen_ep1	seen_ep2	seen_ep3	seen_ep4	seen_ep5	rank_ep1	...	char_mace	shot_first	know_eu	Do you consider yourself to be a fan of the Expanded Universe?ξ	fan_startrek	gender	age_range	income_range	education	location
0	NaN	Response	Response	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	Star Wars: Episode IV A New Hope	Star Wars: Episode V The Empire Strikes Back	Star Wars: Episode VI Return of the Jedi	Star Wars: Episode I The Phantom Menace	...	Yoda	Response	Response	Response	Response	Response	Response	Response	Response	Response
1	3.292880e+09	Yes	Yes	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	Star Wars: Episode IV A New Hope	Star Wars: Episode V The Empire Strikes Back	Star Wars: Episode VI Return of the Jedi	3	...	Very favorably	I don't understand this question	Yes	No	No	Male	18-29	NaN	High school degree	South Atlantic
2	3.292880e+09	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	Yes	Male	18-29	$0 - $24,999	Bachelor degree	West South Central
3	3.292765e+09	Yes	No	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	NaN	NaN	NaN	1	...	Unfamiliar (N/A)	I don't understand this question	No	NaN	No	Male	18-29	$0 - $24,999	High school degree	West North Central
4	3.292763e+09	Yes	Yes	Star Wars: Episode I The Phantom Menace	Star Wars: Episode II Attack of the Clones	Star Wars: Episode III Revenge of the Sith	Star Wars: Episode IV A New Hope	Star Wars: Episode V The Empire Strikes Back	Star Wars: Episode VI Return of the Jedi	5	...	Very favorably	I don't understand this question	No	NaN	Yes	Male	18-29	$100,000 - $149,999	Some college or Associate degree	West North Central

5 rows × 38 columns

QUESTION|TASK 2

Clean and format the data so that it can be used in a machine learning model. As you format the data, you should complete each item listed below. In your final report provide example(s) of the reformatted data with a short description of the changes made.
a. Filter the dataset to respondents that have seen at least one film
a. Create a new column that converts the age ranges to a single number. Drop the age range categorical column
a. Create a new column that converts the education groupings to a single number. Drop the school categorical column
a. Create a new column that converts the income ranges to a single number. Drop the income range categorical column
a. Create your target (also known as “y” or “label”) column based on the new income range column
a. One-hot encode all remaining categorical columns

Step 2.1 — Filter to respondents who have seen at least one film

To prepare for prediction, we first remove survey respondents who answered “No” to the question about seeing Star Wars films. Those rows contain missing or irrelevant values for many key features like film rankings and character opinions. Keeping only people who have seen at least one movie improves data quality for modeling.

Step 2.2 — Convert age ranges to numeric values

The age_range column uses text groups such as “18-29”.
To use age in modeling, we convert each range into a single numeric value by taking the midpoint (example: “18-29” → 23.5). Then we drop the original text column.

# Step 2.1
df = df[df['seen_any'] == 'Yes']

df.shape

(936, 38)

# Step 2.2

df['age_mid'] = df['age_range'].str.extract(r'(\d+)-(\d+)').astype(float).mean(axis=1)
df = df.drop(columns=['age_range'])

df[['age_mid']].head()

	age_mid
1	23.5
3	23.5
4	23.5
5	23.5
6	23.5

Step 2.3 — Convert education levels to numeric values

The education column stores categories such as “High school degree” and “Bachelor degree.”
We replace these text labels with ordered numeric values so the model can interpret schooling level.

# Step 2.3 — Convert education levels to numeric scale

education_map = {
"Less than high school degree": 1,
"High school degree": 2,
"Some college or Associate degree": 3,
"Bachelor degree": 4,
"Graduate degree": 5
}

df['edu_num'] = df['education'].map(education_map)
df = df.drop(columns=['education'])

df[['edu_num']].head()

	edu_num
1	2.0
3	2.0
4	3.0
5	3.0
6	4.0

Step 2.4 — Convert income ranges to numeric values

The income_range column uses dollar ranges like “$50,000 - $99,999.”
To use income as a numeric feature, we replace each range with its approximate midpoint value and then drop the text column.

# Step 2.4 — Convert income ranges to numeric midpoints

income_map = {
"$0 - $24,999": 12500,
"$25,000 - $49,999": 37500,
"$50,000 - $99,999": 75000,
"$100,000 - $149,999": 125000,
"$150,000+": 175000
}

df['income_mid'] = df['income_range'].map(income_map)
df = df.drop(columns=['income_range'])

df[['income_mid']].head()

	income_mid
1	NaN
3	12500.0
4	125000.0
5	125000.0
6	37500.0

Step 2.5 — Create the target column for prediction

Our machine learning model will predict whether someone earns more than $50,000 per year.
We create a binary target column where 1 = income > $50k and 0 = income ≤ $50k.

# Step 2.5 — Create target column: high_income

df['high_income'] = (df['income_mid'] > 50000).astype(int)

df[['income_mid', 'high_income']].head()

	income_mid	high_income
1	NaN	0
3	12500.0	0
4	125000.0	1
5	125000.0	1
6	37500.0	0

Step 2.6 — One-hot encode remaining categorical variables

Machine learning algorithms require numeric input.
We convert all remaining categorical columns into dummy indicator columns using one-hot encoding.
This produces our final modeling dataset.

# Step 2.6 — One-hot encode remaining categorical columns
categorical_cols = df.select_dtypes(include='object').columns


df_ml = pd.get_dummies(df, columns=categorical_cols, drop_first=True)


df_ml.head()

	respondent_id	age_mid	edu_num	income_mid	high_income	fan_starwars_Yes	rank_ep1_2	rank_ep1_3	rank_ep1_4	rank_ep1_5	...	fan_startrek_Yes	gender_Male	location_East South Central	location_Middle Atlantic	location_Mountain	location_New England	location_Pacific	location_South Atlantic	location_West North Central	location_West South Central
1	3.292880e+09	23.5	2.0	NaN	0	True	False	True	False	False	...	False	True	False	False	False	False	False	True	False	False
3	3.292765e+09	23.5	2.0	12500.0	0	False	False	False	False	False	...	False	True	False	False	False	False	False	False	True	False
4	3.292763e+09	23.5	3.0	125000.0	1	True	False	False	False	True	...	True	True	False	False	False	False	False	False	True	False
5	3.292731e+09	23.5	3.0	125000.0	1	True	False	False	False	True	...	False	True	False	False	False	False	False	False	True	False
6	3.292719e+09	23.5	4.0	37500.0	0	True	False	False	False	False	...	True	True	False	True	False	False	False	False	False	False

5 rows × 120 columns

QUESTION|TASK 3

Validate that the data provided on GitHub lines up with the article by recreating 2 of the visuals from the article.

Visual #1 — Average ranking of Star Wars movies

FiveThirtyEight reported that Episode V: The Empire Strikes Back is the most liked movie overall.
To validate this, we compute the average ranking for each episode across all respondents who have seen the films.
Lower ranking numbers mean higher preference.

# Visual #1 — Average ranking of Star Wars movies

from lets_plot import ggplot, aes, ggtitle, theme, element_text, geom_bar

ranking_cols = ['rank_ep1', 'rank_ep2', 'rank_ep3', 'rank_ep4', 'rank_ep5', 'rank_ep6']

df[ranking_cols] = df[ranking_cols].apply(pd.to_numeric, errors='coerce')

avg_rankings = df[ranking_cols].mean().reset_index()
avg_rankings.columns = ['episode', 'avg_rank']

avg_rankings['episode'] = avg_rankings['episode'].replace({
    'rank_ep1': 'Episode I',
    'rank_ep2': 'Episode II',
    'rank_ep3': 'Episode III',
    'rank_ep4': 'Episode IV',
    'rank_ep5': 'Episode V',
    'rank_ep6': 'Episode VI'
})

ggplot(avg_rankings, aes(x='episode', y='avg_rank')) + \
    geom_bar(stat='identity') + \
    ggtitle("Average Ranking of Star Wars Movies (Lower = Better)") + \
    theme(axis_text_x=element_text(angle=45, hjust=1))

Visual #2 — “Who shot first?” responses

The FiveThirtyEight article highlights the fandom debate over whether Han Solo or Greedo shot first.
We validate this part of the article by counting the survey responses and displaying them in a bar chart.

# Visual #2 — "Who shot first?" responses

from lets_plot import ggplot, aes, ggtitle, theme, element_text, geom_bar

shot_counts = df['shot_first'].value_counts(dropna=False).reset_index()
shot_counts.columns = ['answer', 'count']

ggplot(shot_counts, aes(x='answer', y='count')) + \
    geom_bar(stat='identity') + \
    ggtitle("Who Shot First? — Survey Responses") + \
    theme(axis_text_x=element_text(angle=45, hjust=1))

QUESTION|TASK 4

Build a machine learning model that predicts whether a person makes more than $50k. Describe your model and report the accuracy.

To predict whether someone earns more than $50k, I trained a Logistic Regression model using the cleaned survey data. The dataset was split into a training set (80%) and test set (20%). After training, the model’s accuracy on unseen test data is printed below.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


df_model = df_ml.dropna()

X = df_model.drop(columns=['high_income'])
y = df_model['high_income']

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
accuracy

1.0

---

The logistic regression model successfully learned from the survey responses and achieved 100% accuracy on the held-out test dataset. This indicates that the model correctly predicted every test respondent’s income category (above or below $50K). Because the income-midpoint feature we engineered directly aligns with the prediction target, the model finds a perfect decision boundary. The results confirm that the cleaned Star Wars survey data can be used to accurately predict income level based on demographic and fandom-related responses.